






'''simple docstring'''







import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=13   ,	UpperCamelCase__=30   ,	UpperCamelCase__=2   ,	UpperCamelCase__=3   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=32   ,	UpperCamelCase__=5   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=10   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=3   ,	UpperCamelCase__=0.6   ,	UpperCamelCase__=None   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			image_size
         a_						     =			patch_size
         a_						     =			num_channels
         a_						     =			is_training
         a_						     =			use_labels
         a_						     =			hidden_size
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			intermediate_size
         a_						     =			hidden_act
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			type_sequence_label_size
         a_						     =			initializer_range
         a_						     =			mask_ratio
         a_						     =			scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         a_						     =			(image_size // patch_size) ** 2
         a_						     =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         a_						     =			None
         if self.use_labels:
                a_						     =			ids_tensor([self.batch_size]   ,	self.type_sequence_label_size	)

         a_						     =			self.get_config()

         return config, pixel_values, labels







  def    _a       (			self	):






         """simple docstring"""


         return ViTMAEConfig(
             image_size=self.image_size   ,	patch_size=self.patch_size   ,	num_channels=self.num_channels   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	is_decoder=UpperCamelCase__   ,	initializer_range=self.initializer_range   ,	mask_ratio=self.mask_ratio   ,	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			ViTMAEModel(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__	)
         self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			ViTMAEForPreTraining(UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(UpperCamelCase__	)
         a_						     =			(self.image_size // self.patch_size) ** 2
         a_						     =			self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, num_patches, expected_num_channels)	)

         # test greyscale images
         a_						     =			1
         a_						     =			ViTMAEForPreTraining(UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
         a_						     =			model(UpperCamelCase__	)
         a_						     =			self.patch_size**2
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, num_patches, expected_num_channels)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         a_     ,	a_     ,	a_						     =			config_and_inputs
         a_						     =			{'pixel_values': pixel_values}
         return config, inputs_dict





@require_torch
class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Optional[int]      			=     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  _a  : Optional[Any]      			=     {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

  _a  : List[Any]      			=     False
  _a  : Any      			=     False
  _a  : str      			=     False
  _a  : Tuple      			=     False







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ViTMAEModelTester(self	)
         a_						     =			ConfigTester(self   ,	config_class=UpperCamelCase__   ,	has_text_modality=UpperCamelCase__   ,	hidden_size=37	)







  def    _a       (			self	):






         """simple docstring"""


         self.config_tester.run_common_tests()







  @unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                self.assertIsInstance(model.get_input_embeddings()   ,	(nn.Module)	)
                a_						     =			model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(UpperCamelCase__   ,	nn.Linear	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                a_						     =			inspect.signature(model.forward	)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                a_						     =			[*signature.parameters.keys()]

                a_						     =			['pixel_values']
                self.assertListEqual(arg_names[:1]   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         np.random.seed(2	)

         a_						     =			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
         a_						     =			torch.from_numpy(UpperCamelCase__	)

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         a_						     =			pt_noise

         super().check_pt_tf_models(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                a_						     =			model_class(UpperCamelCase__	)
                model.to(UpperCamelCase__	)
                model.eval()
                # make random mask reproducible
                torch.manual_seed(2	)
                with torch.no_grad():
                       a_						     =			model(**self._prepare_for_class(UpperCamelCase__   ,	UpperCamelCase__	)	)

                a_						     =			outputs[0].cpu().numpy()
                a_						     =			0

                with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(UpperCamelCase__	)
                       a_						     =			model_class.from_pretrained(UpperCamelCase__	)
                       model.to(UpperCamelCase__	)
                       # make random mask reproducible
                       torch.manual_seed(2	)
                       with torch.no_grad():
                              a_						     =			model(**self._prepare_for_class(UpperCamelCase__   ,	UpperCamelCase__	)	)

                       # Make sure we don't have nans
                       a_						     =			after_outputs[0].cpu().numpy()
                       a_						     =			0
                       a_						     =			np.amax(np.abs(out_a - out_a	)	)
                       self.assertLessEqual(UpperCamelCase__   ,	1e-5	)







  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
  def    _a       (			self	):






         """simple docstring"""


         pass







  @slow
  def    _a       (			self	):






         """simple docstring"""


         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_						     =			ViTMAEModel.from_pretrained(UpperCamelCase__	)
                self.assertIsNotNone(UpperCamelCase__	)





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image




@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None







  @slow
  def    _a       (			self	):






         """simple docstring"""


         np.random.seed(2	)

         a_						     =			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	).to(UpperCamelCase__	)

         a_						     =			self.default_image_processor
         a_						     =			prepare_img()
         a_						     =			image_processor(images=UpperCamelCase__   ,	return_tensors='pt'	).to(UpperCamelCase__	)

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         a_						     =			ViTMAEConfig()
         a_						     =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
         a_						     =			np.random.uniform(size=(1, num_patches)	)

         # forward pass
         with torch.no_grad():
                a_						     =			model(**UpperCamelCase__   ,	noise=torch.from_numpy(UpperCamelCase__	).to(device=UpperCamelCase__	)	)

         # verify the logits
         a_						     =			torch.Size((1, 196, 768)	)
         self.assertEqual(outputs.logits.shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor(
             [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]	)

         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,	expected_slice.to(UpperCamelCase__	)   ,	atol=1e-4	)	)












'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




